Early Clinical Outcomes of the Novel Hinotori Robotic System in Urological Surgery—A Review of Existing Literature
Bibliographic record
Abstract
Robotic-assisted surgery has gradually established its role in uro-oncological cases that demand a high level of precision, optimising surgeon ergonomics and decreasing fatigue whilst maintaining optimal clinical outcomes. With the novel Hinotori surgical robot (Medicaroid Corporation (Kobe, Hyogo, Japan)) launched in Japan back in 2019, it has now demonstrated its use case across various clinical series of different surgeries. We sought to narratively synthesise the initial feasibility of the Hinotori robotic system in urology. A systematic, comprehensive literature search was conducted across various databases from September 2024 to October 2024. Relevant keywords within the scope of this study were generated for a more accurate search. After exclusion and removal of duplicates, a total of nine articles were included for review. Among the included studies, one study reported data solely on radical prostatectomy for prostate cancer, two studies reported on robotic-assisted nephroureterectomy for renal tumours, two studies reported on partial nephrectomy performed for renal masses, two studies reported on radical nephrectomy carried out for renal malignancies and one study reported on robotic-assisted adrenalectomy for adrenal cancer. Lastly, one study collectively reported on outcomes pertaining to partial nephrectomy, partial nephrectomy, vesicourethral anastomosis and pelvic lymph node dissection in a porcine model, as well as partial nephrectomy, radical prostatectomy and pelvic lymph node dissection in cadavers. The current literature supports its non-inferiority to the well-established Da Vinci system, with no major drawbacks or concerns identified when comparing parameters such as intraoperative time, estimated blood loss (EBL), perioperative events (transfusions, conversion to open surgery), length of hospital stay and major postoperative complications. Future studies involving larger cohorts and more complex surgical cases are essential to further evaluate the efficacy and safety of the Hinotori system. The new Hinotori robotic system offers unique three-dimensional features as a non-inferior robotic platform alternative that has proven clinically safe thus far in its use. Larger scale studies and randomised trials are eagerly awaited to assess and validate more holistically its clinical utility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".